Pipeline networks are ubiquitous for transporting fluids. Whether for the transmission/distribution of gas, oil, water or within an industrial process, pipeline networks play a critical operational role. Failure modes may result in a wide spectrum of negative effects ranging from energy loss to revenue loss to catastrophic failure with loss of life. The present invention introduces innovative technology to detect, identify and locate the following anomalous events representing failure modes and/or precursors to failure modes within a pipeline network:                Leaks in pipelines        Tuberculated pipeline sections        Partially closed or fully closed valve gates        Variations in fluid homogeneity, e.g., air pockets within a water distribution network        Pipe wall structural degradation        Biofilm accumulation        Deviation from the utility's as-built pipeline network plans, e.g., unknown branches, unknown valves, and/or other unknown structural features.        
In evaluating acoustic signal propagation characteristics within a conduit with a gaseous medium, it is usually assumed that the wall motion is negligible and the evaluation can be conducted assuming a rigid waveguide. This assumption may no longer hold when the conduit is used to transport fluids, since the elastic moduli and densities of the conduit wall and the fluid are often of similar magnitudes, making the rigid wall approximation invalid. The present invention utilizes acoustic signals in order to assess a pipeline for anomalous events. As part of this assessment, acoustic signal propagation needs to be characterized within the conduit.
The axisymmetric traveling wave of angular radial frequency, ω, has been evaluated by Del Grosso as reported in Baik [1] and Lafleur [2]. The analytical results provide a method for estimating the axial and radial components of particle displacement at position (r,z) and time t of an acoustic wave within a pipe. To distinguish the modes under the assumptions for this system, e.g., elastic wall with finite thickness, Del Grosso introduced the notation ETm, m=0, 1, 2, . . . , (E indicates elastic wall and T finite thickness). This corresponds to the standard notation for modal analysis Lmn(⋅), with the integers mn identifying the mode of the wave traveling along an axis. Limiting the analysis to axisymmetric, the first integer is zero. Baik [1] and Lafleur [2] extend Del Grosso's work through theoretical development supported with empirical studies. Their results indicate acoustic propagation within a conduit is dependent on a number of factors including: pipe diameter, pipe wall thickness, fluid density, pipe wall density, intrinsic sound velocity in the fluid, and intrinsic radial and axial sound velocity in the pipe wall. Parameter variation has a significant impact on the number of acoustic modes, ETm, supported in the fluid, as well as each supported mode's particle displacement and phase velocity within the fluid.
Additional insight into acoustic propagation is given by Long et. al. [3] where wave propagation in a soil-pipe-water tri-layer system is evaluated based on a model developed by the authors called DISPERSE. The study focuses on using the model to assist in evaluating leak detection based on correlating accelerometer data collected at two water valves. The study results indicate the soil density and its intrinsic radial and axial sound velocity within it can substantially impact the characteristics of the acoustic signal propagation within the pipe's fluid.
Estimating acoustic propagation characteristics within the pipeline being assessed is an important aspect of the present invention. The current art indicates analytical evaluation is complex with significant uncertainty in predicting parameters associated with the evaluation. The present invention utilizes the insight provided by the analytical model and couples this insight with active and passive acoustic measurement results of each pipeline.
Acoustic signals have been extensively used for pipeline assessment [4]. Prior art indicates a general dichotomy for classifying the acoustic based technologies: passive and active. Passive acoustic technologies refer to using a hydrophone and/or accelerometer to detect the presence of acoustic waves indicative of a failure mode. Active acoustic technologies involve using an active signal source to generate an acoustic signal, i.e., an acoustic transmission. The acoustic transmission is used to interrogate the pipeline and the received signal from the transmission is used to detect a failure mode. Active acoustic technologies can be further subdivided as reflective and transmittive. Reflective technologies exploit the reflected transmitted signal, whereas the transmittive technologies exploit the transmitted signal through the pipeline.
The following prior art introduces the use of hydrophone/projectors for acoustic communications within a water distribution network. The technical papers and patents use well established principles in digital communications to provide communications between two or more points within a water distribution network. Acevedo et. al. [5] illustrated acoustic communication between two points within a pressurized water pipe through an experimental setup. Kikossalakis [6] [7] provides a theoretical development and simulation for communication within a pressurized water pipe with a method for powering the system using an energy harvesting technology. Vladimir's [8] invention outlines an in-pipe acoustic communication system for controlling water pressure by transmitting control signals through the pipe via acoustic transmitter and receiver. Martin and Cooper's [9] invention presents a point-to-point acoustic communication system for in-pipe in the frequency range of 3-100 kHz. Cooper and Burnham's [10] invention provides a system for sensing and communicating in a pipeline that contains a fluid. Their invention provides a warning of unauthorized contamination or accidental contamination by sensing and communicating.
The following prior art exploits passive acoustic for leak detection within a fluid filled container. Greene, et al.'s [11] invention provides a system for detecting and mapping acoustic noise intensity in a three-dimensional noise field and uses it to infer operational or performance characteristics. Chana's method and apparatus [12] provide a system of locating leaks within a network of pipes using two or more acoustic sensors with data loggers. The recorded acoustic signals are adjusted for temperature prior to correlations and the signal processing is further enhanced based on extended sound data sets used to reduce noise. Multiple acoustic sensors are used to improve the sound velocity estimate within the pipe. Kurisu, et al.'s system [13] uses pressure sensors to monitor and detect the acoustic wave generated by a breakage or leak within a pipeline. Location is estimated based on correlating the time difference between the event detection times at the sensors. Lander and Saltzstein's methods are based on using multiple acoustic sensors [14] or multiple vibration sensors [15] for detecting and locating acoustic signal caused by leak. Lander extends the use of vibration sensors for monitoring a pipeline network for leaks [16]. Chang's system [17] uses a string of microphones attached to the exterior of a pipeline to detect leaks in the vicinity of the peak response. Savic's system [18] uses multiple acoustic sensors for leak detection. Detection and location estimate is based on using a distributed parameter acoustic model of the buried pipeline based on an autoregressive moving average (ARMA) filter. Suzuki et al.'s system [19] uses an array of vibration signal detectors to detect and locate leaks based on cross correlation. Hunaidi's system [20] detects and locates leaks in plastic water distribution pipes by detecting the acoustic signal induced by the leak measured at two or more locations via vibration sensors or hydrophones. Location is estimated based on a cross-correlation function or an enhanced impulse response function. Bseisu et al.'s system [21] senses both axial and torsional vibrations and pressure fluctuations caused by a leakage event. The location of the leak is determined by comparing the travel time of the selected pairs of both axial and torsional signals. Roberts et al.'s method and apparatus [22] use transducers spaced along a pipe to detect acoustic energy caused by a leak or third party strike. Detecting and locating the source of the leak is based on modal analysis of the received signal and then generalized cross correlation is used on selected modes to identify and locate the source. Yang and Recane's invention [23] uses acoustic sensors to detect and locate an acoustic signal. A matched filter is employed on the received signal in order to reduce false alarms and improve sensitivity for leak detection. Paulson's process and apparatus [24] use acoustic monitoring to indicate possible leak locations; the pipeline is also monitored for temperature. This method correlates acoustics and temperature to detect possible pipeline leaks and their locations. Yang, et al.'s invention [25] correlates acoustic pressure sensor measurements in conjunction with strain gauge measurements to detect and locate leaks in a pipeline. Joel and Pascal provide a method [26] for reducing false alarms for detecting leaks and strikes based on acoustic sensor measurements.
The following prior art exploits passive acoustics for parameter characterization within a fluid filled container. Lapinski, et al.'s invention [27] presents a method and system for determining the direction of fluid flow using one or more acoustic transducers in proximity to a conduit. The characteristics of the acoustic noise sources detected by the acoustic transducers within the conduit are used to determine the direction of flow. Okada et al.'s [28] invention provides monitoring and location estimation for leak and third party strikes based on using multiple acoustic sensors attached to the wall or inserted within the pipe. Detection is based on identifying abnormal acoustic events occurring within the pipe such as leaks or pipe strikes and the event's location is evaluated by using the relative time difference in the arrival of the acoustic signal at the sensors. Russo's invention [29] uses an acoustic signal induced by a leak to detect the event and location within a steam pipe. Worthington and Worthington's system [30] uses multiple hydrophones installed at valve locations to detect and locate sounds emanating from a breaking, moving or re-anchoring reinforcement within a pre-stressed concrete cylinder. Paulson's invention [31] uses an array of acoustic sensors to detect leakage and/or reinforcement wire breaking events within pipes. The sensors are either placed along a cable inside the pipe or are installed at regular intervals along the pipe. Events are located by evaluating time of arrival at multiple sensors. Martinek's invention [32] uses an integrated sensor to measure flow rate and direction, water pressure and flow noise. Multiple sensors are deployed and the data collected are correlated for detecting water losses and leak detection in a water distribution network. Bassim and Nabil's apparatus [33] uses multiple acoustic sensors deployed along a pipeline that detect long term acoustic emissions which are indicative of failure modes. Allison, et al.'s system [34] detects impacts to a pipeline using acoustic detection with hydrophones. Hydrophones directly measure acoustic signals propagated along the pipeline due to impact. Haines and Francini's system [35] detects the contact with an in-ground pipeline via acoustic sensors where each sensor is employed to detect a different parameter. The difference in the travel time between the two parameters is used to determine the location. Dalmazzone, et al.'s system and method [36] use a similar approach as [35], but targets underwater pipelines. Staton and Peck's invention [37] uses both acoustic sensors and seismic sensors to detect drill penetration through a sewer pipe wall during horizontal boring.
The following prior art exploits reflected energy for assessment. Piesinger's [38] invention uses a pseudo noise (PN) modulated electrical signal applied to an electrical distribution network. Reflected energy from the PN signal in conjunction with network knowledge is used in detecting faults within the network. Fink's invention [39] provides a method for evaluating the impulse response in a reflective medium based on using multiple (two or more) transducers to simultaneously excite the medium with orthogonal acoustic signals. Harley's invention [40] discloses a method for examining a body based on transmitting N continuous orthogonal signals into the body. The N signals and reflections are recorded and then used to produce a wavefield and measure travel time, which can be used to characterize changes in the reflective body. Ledeen, et al.'s method and apparatus [41] are a monitoring system which detects and locates a leak in a pipeline. The system is based on first detecting the acoustic signal generated by the leak. The system then uses a co-located pressure transmitter to generate an acoustic wave. The reflected acoustic wave from the leak is used to determine its location. Shamout et al.'s invention [42] is based on using a single acoustic transmitter and one or more acoustic sensors. The reflected signals from the acoustic transmission are used to detect blockages and leaks. A reference signal for the pipe segment under non-leak and non-blockage condition is used to detect abnormalities. The multiple acoustic sensors are used to determine the direction of the reflected signal.
The following prior art exploits active acoustic for parameter characterization within a fluid filled container. Baumoel's system [43] detects and locates leaks using the effect of pressure drop on acoustic signals. An acoustic signal is induced into the pipe wall and within the flow. A leak in the pipe creates an area of low pressure causing the acoustic wave within the flow to be delayed. Hill's invention [44] uses an acoustic pulse in conjunction with an integrated optic fiber for acoustic sensing. The optic fiber is positioned along the path and outside the conduit. A profile of the conduit condition can be derived by monitoring the acoustic signal as it is transmitted through the conduit. Howitt's method and system [45] use an acoustic transmitter and acoustic receiver at either end of a pipeline segment in order to assess the blockage within the pipe.
The following prior art exploits pipeline network analysis based on monitored data. Mizushina et al's method [46] for estimating the location of leaks within pipes is based on measuring flow rates or pressures arranged at multiple points within the pipe network. Peleg et al.'s system and method [47] use utility metering and monitoring data to statistically evaluate water network events including leakage events. Scolnicov et al.'s system and method [48] use event data from multiple sensors in a water network and use the event data to detect and identify related anomalous events. Wakamori et al.'s method [49] estimates the fracture point location(s) in a pipe network based on pressure variations and flow continuity requirements monitored at multiple locations throughout the network. Farmer's system [50] of monitoring for leaks within a pipeline uses statistical analysis based on pressure or flow data collected at multiple locations. In Abhulimen and Susu's method [51] for detecting and locating leaks in a pipeline network, flow models are used to characterize both the steady and unsteady state flow behavior corresponding to absence and presence of modeled leaks, respectively. Liapunov's stability theory is used in evaluating the leak status based on the flow models. Guidi and Tedeschi's method [52] for leak detection is based on statistical analysis of the good state and the bad state estimated by monitoring the input flow rate to the network. Yukawa, et al.'s method [53] for water leakage detection and location estimation is based on flow meter and pressure gauge data integrated with a flow model. Greenlee, et al.'s system and method [54] is based on comparing flow vector models, one based on known conditions and one based on observed flow data. Leak detection is based on the comparison.
In summary, a primary pipeline assessment application is leak detection, which often exploits passive acoustics for detecting the pressure wave generated by the leak. This approach is susceptible to background noise and care needs to be taken to reduce false positives and false negatives. In addition, location estimation requires knowing the intrinsic acoustic velocity within the pipeline. Errors in predicting the velocity within the pipeline will directly impact the location estimation error. Related prior art also provides insight in using reflected acoustic detection from an active acoustic transmission for detecting/locating leaks and blockages. This prior art does not exploit the information stored in the passive detection nor the active transmittive signal through the pipeline. Related art also provides insight into using pipeline network monitoring devices to evaluate and assess water losses within the network. This prior art provides information for identifying areas within the network requiring additional investigation, but are less reliable for identifying specific pipelines and the location on the specific pipeline causing the leak or other anomalous events.